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Abstract

This chapter focuses on the multi-objective methodology to establish an evaluation model for returned components and products. The chapter starts with an introduction about the issue of remanufacturability and the importance of the product information technology. Then, the related studies dealing with similar problems in the literature are discussed in the background section. Next, the focal problem of this chapter is stated in the problem statement section. A detailed description about the approaches (i.e., fuzzy logic and Bayesian approaches) can be found in the proposed methodology section. Right after this, an illustrative example is explained in the experimental study section. The potential research directions regarding the main problem considered in this chapter are highlighted in the future trends section. Finally, the conclusion drawn in the last section closes this chapter.

Introduction

After their successful collection, the used products should be properly pre-sorted/classified in order to easy for the further processing (Galbreth & Blackburn, 2006; Loomba & Nakashima, 2012; Tagaras & Zikopoulos, 2008). The specific objective of sorting operation is to identify which used products should be remanufactured and which should be scrapped. In general, there are two different sorting processes: visually sorting and task a test. Often a visual scan is the most reliable way to evaluate a product’s state. However, when the state of more complex components shall be evaluated, this may not be sufficient. In addition to the visual scanning, the remanufacturer often needs to evaluate the used products’ remanufacturability before the returns enter the remanufacturing process.

Many researchers pointed out that to evaluate used products’ remanufacturability before the remanufacturing process performed is a significant research problem (Wu, 2012). Also, remanufacturability should be considered at the early stage of product design in order to increase the potential for product remanufacturing (Hatcher, Ijomah, & Windmill, 2011). In addition, several concepts and methodologies have been proposed to deal with the remanufacturability and thus increase the efficiency of the whole remanufacturing processes. For example, Yüksel (2010) studied the quality function deployment (QFD) and the house of quality (HOQ) methodology to enhance the remanufacturability. In (Sundin, Björkman, & Jacobson, 2000; Sundin & Bras, 2005), the authors discussed the opportunity for improving remanufacturability within a “product-service system”. By using graph-based methodologies, (Güngör & Gupta, 1997; Kang, Lee, Xirouchakis, & Persson, 2001; Lambert, 1997; Mello & Sanderson, 1990) focused on the problem of assessing used products ease of disassembly which is the building block of remanufacturability evaluation. Among others, identifying and providing of relevant criteria as guidelines for design and/or evaluate remanufacturability is the most used methodology (Amezquita, Hammond, Salazar, & Bras, 1995; W. Ijomah, 2009; Winifred L. Ijomah, Christopher A. McMahon, Geoffrey P. Hammond, & Stephen T. Newman, 2007a; Winifred L. Ijomah, C.A. Mcmahon, G.P. Hammond, & S.T. Newman, 2007b). These guidelines not only offer a good starting point for developing an assessment tool, but may also be linked to a particular remanufacturing concern (Hatcher et al., 2011), such as economic benefits, green thinking, and some specific operations.

One of the major hampering on managing such guidelines is the paucity of readily available information. Even though there is some information about the new products such as geometry, bill of material, flow chart, etc., however, information related to the used products such as the product use pattern with respect to exact usage conditions, current state, and remaining life of products is either missing or incomplete. Also, the product information is usually scattered, disorganized and machine unreadable (Zhu, Sarigecili, & Roy, 2013). Moreover, appropriate exchange of information is frequently counteracted by an intense competition between the enterprises concerned (Jacobsson, 2000). With these consideration in mind, a lot of remanufacturers face the problem between limited information about the feedback of forward cycle features (including manufacturing, shipping, transportation and delivery), and the forecast of reverse cycle features (including users’ behaviour, physical changes, maintenance information, and suitable design for remanufacturing specifications).